Differentiating between benign and malignant bone lesions: The value of using SPECT/CT
Bibliographic record
Abstract
Two cases are presented; in each case a patient with a known cancer history was imaged with combined single photon emission computed tomography (SPECT) and computed tomography (CT) to evaluate suspected bone pathology. While SPECT detected focal increased radiotracer uptake in the iliac bone in both patients, CT suggested a distinct etiology. In one case, the bone abnormality included lytic cortical bone destruction with soft tissue extending beyond the confines of the bone, consistent with osseous metastasis. In the other case there was loss of bone cortical integrity but an otherwise preserved cortical margin, lack of soft tissue involvement, and intralesional fat suggestive of a benign intraosseous lipoma. These two cases underscore the value of using hybrid imaging to distinguish benign from malignant bone disease; the structural details provided by CT were helpful to derive the diagnosis, potentially impacting patient management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".